File(s) not publicly available
Behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigation
journal contribution
posted on 2023-06-08, 10:23 authored by Jose A Fernandez-Leon, Gerardo G Acosta, Miguel A MayoskThis paper deals with the study of scaling up behaviors in evolutive robotics (ER). Complex behaviors were obtained from simple ones. Each behavior is supported by an artificial neural network (ANN)-based controller or neurocontroller. Hence, a method for the generation of a hierarchy of neurocontrollers, resorting to the paradigm of Layered Evolution (LE), is developed and verified experimentally through computer simulations and tests in a Khepera® micro-robot. Several behavioral modules are initially evolved using specialized neurocontrollers based on different ANN paradigms. The results show that simple behaviors coordination through LE is a feasible strategy that gives rise to emergent complex behaviors. These complex behaviors can then solve real-world problems efficiently. From a pure evolutionary perspective, however, the methodology presented is too much dependent on user’s prior knowledge about the problem to solve and also that evolution take place in a rigid, prescribed framework. Mobile robot’s navigation in an unknown environment is used as a test bed for the proposed scaling strategies.
History
Publication status
- Published
Journal
Robotics and Autonomous SystemsISSN
09218890Publisher
ElsevierExternal DOI
Issue
4Volume
57Page range
411-419Department affiliated with
- Informatics Publications
Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2012-02-20Usage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC